Gamification implies the application of methods and design patterns from gaming to non-gaming areas like learning or working. We applied an existing gamification design to production processes in an organization which provides sheltered employment for impaired persons.

In contrast to existing work, we investigated not only a short period but a complete workday to measure the effects on the performance of impaired workers.

The study indicates that gamification has
(1) a negative effect on workers with considerable cognitive impairments,
(2) no measurable effect on workers with medium cognitive impairments and
(3) a positive effect on workers with mild cognitive impairments.

Presentation

This work will be presented at CHI ’16 (May 7-12) in San Jose, California, USA.

Gamification is an ever more popular method to increase motivation and user experience in real-world settings. It is widely used in the areas of marketing, health and education. However, in production environments, it is a new concept. To be accepted in the industrial domain, it has to be seamlessly integrated in the regular work processes.

In this work we make the following contributions to the field of gamification in production: (1) we analyze the state of the art and introduce domain-specific requirements; (2) we present two implementations gamifying production based on alternative design approaches; (3) these are evaluated in a sheltered work organization. The comparative study focuses acceptance, motivation and perceived happiness.

The results reveal that a pyramid design showing each work process as a step on the way towards a cup at the top is strongly preferred to a more abstract approach where the processes are represented by a single circle and two bars.

Using video game elements to improve user experience and user engagement in non-game applications is called “gamification”. This method of enriching human-computer interaction has been applied successfully in education, health and general business processes. However, it has not been established in industrial production so far.

After discussing the requirements specific for the production domain we present two workplaces augmented with gamification. Both implementations are based on a common framework for context-aware assistive systems but exemplify different approaches: the visualization of work performance is complex in System 1 and simple in System 2.

Based on two studies in sheltered work environments with impaired workers, we analyze and compare the systems’ effects on work and on workers. We show that gamification leads to a speed-accuracy-tradeoff if no quality-related feedback is provided. Another finding is that there is a highly significant raise in acceptance if a straightforward visualization approach for gamification is used.

In the Internet of Things area, sensor-based smart environments are becoming more and more ubiquitous. Smart environments can support user’s cognitive abilities and support them in various tasks e.g. assembling, or cooking.

However, programming applications for smart environments still requires a lot of eort as many sensors
need to be programmed and synchronized. In this work, we present a novel approach for programming procedures in smart environments through demonstrating a task. We define abstract high-level areas that are triggered by the user while performing a task. According to the triggered areas, projected instructions for performing the task again are automatically created. Those instructions can then be transferred to other users to learn how to assemble a product or to cook a meal.

We present a prototypical implementation of a smart environment using optical sensors and present how it can be used in a smart factory and in a smart kitchen.

Recent advances in motion recognition allow the development of Context-Aware Assistive Systems (CAAS) for industrial workplaces that go far beyond the state of the art: they can capture a user’s movement in real-time and provide adequate feedback. Thus, CAAS can address important questions, like Which part is assembled next? Where do I fasten it? Did an error occur? Did I process the part in time? These new CAAS can also make use of projectors to display the feedback within the corresponding area on the workspace (in-situ). Furthermore, the real-time analysis of work processes allows the implementation of motivating elements (gamification) into the repetitive work routines that are common in manual production.

In this chapter, the authors first describe the relevant backgrounds from industry, computer science, and psychology. They then briefly introduce a precedent implementation of CAAS and its inherent problems. The authors then provide a generic model of CAAS and finally present a revised and improved implementation.

Abstract:

While context-aware assistive systems (CAAS) have become ubiquitous in cars or smartphones, most workers in production environments still rely on their skills and expertise to make the right choices and movements. (more…)

Context-aware assistive systems (CAAS) have become ubiquitous in cars or smartphones but not in industrial work contexts: while there are systems controlling work results, context-specific assistance during the processes is hardly offered. As a result production workers still have to rely on their skills and expertise. While un-impaired workers may cope well with this situation, elderly or impaired persons in production environments need context-sensitive assistance.

The contribution of the research presented here is three-fold: (1) We provide a framework for context-aware assistive systems in production environments. These systems are based on motion recognition and use projection and elements from game design (gamification) to augment work. (2) Based on this framework we describe a prototype with respect to both the physical and the software implementation. (3) We present the results of a study with impaired workers and quantifying the effects of the augmentations on work speed and quality.

In this paper, we argue for using in-situ projection to augment a user’s working experience. By recognizing objects on a workplace, the system is able to detect the current step within a workflow. Based on the information about position and orientation of the work-piece, specic feedback can be given – even as a projection on top of the workpiece. So far, our work indicates that this technology is accepted by the industry. Currently, we are investigating the use of gamication elements on the error rate. Additionally, we introduce a model for the conception of context aware assistive systems (CAAS). With our workshop participation, we want to discuss the potentials of in-situ projection at the manual workplace with the participants.

Production work requires a high level of awareness and especially manual assembly work is prone to human errors. At the same time the demand for manual assembly grows. Assistive systems in production environments (ASiPE) have to be augmented to improve the overall performance and reduce skill requirements.

In this study the prototype of an augmented ASiPE is applied in an experiment with impaired persons. It uses in-situ projection (i.e. the projection of work-relevant information directly into the working space, Figures 1, 8) to cognitively assist users in assembly and to improve their inclusion in regular work processes. The aim is to observe their behavior with this new form of human computer interaction and to empirically quantify the effects on performance both in time and quality.

The results show that the augmentation has a catalytic effect: The test subjects assembling slowly could not cope with the augmented ASiPE and performed worse than their counterparts without augmentation. The test subjects who worked faster than average assembled the product significantly better, both with respect to time (14.5% reduction) and especially to quality (45.8% error reduction). The ability to access the potential of augmented workplaces seems to be related to a worker’ cognitive potential which is not adequately mapped by the competence ratios sheltered work organizations currently use.